Design of Active Fractional PID Controller Based on Whale's Optimization Algorithm for Stabilizing a Quarter Vehicle Suspension System

Karam, Zeyad A.; Awad, Osama A.

doi:10.3311/ppee.14904

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Te parameters of the PID algorithm can be self-tuning or tuned by intelligent algorithms such as fuzzy, PSO (particle swam optimization), GA (genetic algorithm), etc. [36][37][38]. Tis is only a SISO (single input-single output) algorithm, i.e., it can only control one object.…”

Section: Control Algorithmsmentioning

confidence: 99%

The Dynamic Model and Control Algorithm for the Active Suspension System

Nguyen

2023

Mathematical Problems in Engineering

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Road surface roughness is the leading cause of vehicle oscillation. The suspension system is used to dampen these oscillations. The active suspension system equipped with a hydraulic actuator is more efficient than the passive one. Therefore, it is used to replace the passive suspension system. The article reviews and analyses models and control algorithms for active suspension systems. In this article, the author mentioned three dynamic models commonly used to simulate vehicle oscillations: a quarter-dynamic model, a half-dynamic model, and a fully dynamic model. Each hydraulic actuator can be considered a state variable in the dynamic model. Besides, the control algorithm for the suspension system is significant. Algorithms like PID (proportional–integral–derivative) and LQR (linear quadratic regulator) will fit the linear model. In contrast, the nonlinear model’s algorithms, such as SMC (sliding mode control), fuzzy, and ANN (artificial neural network), will perform better. Overall, vehicle oscillation can be significantly improved once an active suspension system is used. The contents analysed in this article will be the database for selecting control models and algorithms by other researchers in the future.

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Section: Control Algorithmsmentioning

confidence: 99%

The Dynamic Model and Control Algorithm for the Active Suspension System

Nguyen

2023

Mathematical Problems in Engineering

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“…The AI fuzzy-based control algorithms may efficiently improve using a novel fuzzy search tree [14] or fuzzy artificial neural network (FANN), [15]. Some powerful fractional ordered PID (FOPID) controllers are presented for such nonlinear systems and organized to solve multi-input multi-output models [16]. Soriano, Luis Arturo, et al developed in 2021 a sliding mode control (SMC) algorithm to optimize the energy consumption in the position tracking problem for two degrees of freedom SCARA robot.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificially-intelligent robotic space manipulator using fuzzily-architected nonlinear controllers

2022

IJATEE

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To meet the COVID-19 challenges, the fifth digital industrial wave demands an artificially-intelligent control system advances in selective compliance assembly robot arm (SCARA). Since SCARA has an inherently nonlinear dynamic system and friction rejection, this work develops an artificial intelligent (AI) fuzzy-based nonlinear control algorithm (impedance strategy). Consequently, a generalized dynamic five degree of freedom SCARA (5-Dof SCARA) has mathematically been modelled. These nonlinear controllers have been realized as AI-based fuzzy architectures for the position part from the impedance controller; fuzzy logic proportional derivative (PD) control-type one and Fuzzy logic proportional derivative integral (PID) control type two. These AI architectures regulate the position tracking error of the end effectors' forces. That has been tested using procedures based on half-elliptic and full-elliptic trajectories. Comparatively, the test results are tabulated with related existing published results. Both AI-based controllers have been efficiently dealt with robot nonlinear models and friction rejection. However, the proposed Fuzzy logic PD type one controller has been produced a less-optimum response with the inherent system nonlinearities. Thus, an AI fuzzy-based control algorithm has to be optimally developed to resolve the SCARA sluggishness in achieving tasks. Consequently, a fuzzy logic control (FLC) algorithm of a three-dimension membership function (fuzzy type-2) has been designed to improve the position response of the SCARA redundant robot end-effectors; Fuzzy logic PID type two-controller. The third dimension is dedicated to overcoming uncertain limits in the nonlinear system that leads to a more stable response in the robot end-effector with no oscillation and zero error. The test results of the AI fuzzy-based PID nonlinearcontroller has been successfully manifested the superiority in performance to be properly updated in the space shuttle remote manipulator system. Where the large enchantment in position trajectory reached by FLC type-1 PD controller is 93.166% and 25% in X and Y axis respectively, and the large enhancement by the FLC type-2 PID controller is 95.501% and 31.250% in X and Y-axis respectively.

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“…18 The animals used in this algorithm often have a herd lifestyle, such as whales, ants, bees, etc. [19][20][21] Besides, many other intelligent algorithms have been used to search for the controller's parameters optimally. [22][23][24] The PID controller can only be applied to systems that have an object.…”

Section: Introductionmentioning

confidence: 99%

Design a new control algorithm AFSP (Adaptive Fuzzy – Sliding Mode – Proportional – Integral) for automotive suspension system

Nguyen

2023

Advances in Mechanical Engineering

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When a vehicle is driving, the most significant source of oscillations is the irregularities in the surface of the road. An active suspension is utilized to enhance the vehicle’s stability and ride comfort. In this work, the dynamic model with multi variables is considered to simulate vehicle oscillations based on four excitation cases from the road surface. In addition, this research also established the AFSP complex control solution for an active suspension system. This is a completely novel algorithm with many outstanding advantages. The final control signal of the system is synthesized from the signals of the linear controller PI (Proportional – Integral) and the nonlinear controller SMC (Sliding Mode Control). The controller’s parameters will change continuously depending on the established fuzzy rule. According to simulation outcomes, the vehicle body displacement and acceleration dramatically declined when the AFSP algorithm directed an active suspension. The values of acceleration and displacement do not exceed 80% and 20%, compared to the scenario in which the vehicle just utilized the typical passive suspension system. Besides, a phenomenon of “chattering” also does not appear when combining the controllers. Overall, the efficiency of this algorithm is high. This algorithm can be applied to more complex models.

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Design of Active Fractional PID Controller Based on Whale's Optimization Algorithm for Stabilizing a Quarter Vehicle Suspension System

Cited by 8 publications

References 22 publications

The Dynamic Model and Control Algorithm for the Active Suspension System

The Dynamic Model and Control Algorithm for the Active Suspension System

Artificially-intelligent robotic space manipulator using fuzzily-architected nonlinear controllers

Design a new control algorithm AFSP (Adaptive Fuzzy – Sliding Mode – Proportional – Integral) for automotive suspension system

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